An Adaptive Error Correction Method Using Feature Based Analysis Technique for Machine Performance Improvement ? Part 1: Theory Deterioration

Published

November 1, 1994

Author(s)

J Mou, S Cetinkunt, Alkan Donmez

Abstract

An adaptive error correction method using a feature-based analysis technique for improving the accuracy of CNC machine tools is presented. The method described in this paper emphasizes the integration of the process-intermittent gauging and analysis techniques with information from pre-process characterization and post-process inspection. The proposed method utilizes the information from pre-process characterization, process-intermittent gauging, and post-process inspection to improve machine performance automatically. Algorithms are derived for analyzing the post-process and process-intermittent inspection data to decouple process-related errors from machine errors and for identifying the residual systematic errors. A feature-based analysis technique is developed to relate the dimensional and form errors of manufactured features to the systematic machine tool errors. Inverse kinematics and statistical methods are used to identify and characterize the contribution of each residual error component on imperfect features. Also a recursive tuning algorithm is developed for fine tuning the geometric-thermal model.

Proceedings Title

Proceedings of the ASME Winter Annual Meeting Symposium of Intelligent Machine Tool Systems

Volume

Vol.117

Conference Dates

November 13-18, 1994

Conference Location

Chicago, IL, USA

Conference Title

ASME Winter Annual Meeting Symposium of Intelligent Machine Tool Systems

Pub Type

Conferences

Keywords

Error compensation, Machine tool error, Machine tool modeling, Machining, Manufacturing Equipment, Process Control

Citation

Mou, J. , Cetinkunt, S. and Donmez, A. (1994), An Adaptive Error Correction Method Using Feature Based Analysis Technique for Machine Performance Improvement ? Part 1: Theory Deterioration, Proceedings of the ASME Winter Annual Meeting Symposium of Intelligent Machine Tool Systems, Chicago, IL, USA (Accessed December 13, 2024)